Research On Arrhythmia Detection Algorithm Based On Deep Learning

Arrhythmic detection has significant research significance for early detection of cardiovascular diseases and prevention of sudden cardiac death.With the development of big data and the updating of computer hardware,thanks to the improvement of computing power,machine learning and deep learning methods have gradually become mainstream.This article is based on deep neural networks for research.The main work is as follows.(1)Aiming to solve the problem that the amount of information for one-dimensional convolution learning on ECG heartbeat is small,the method of converting data to two-dimensional matrix is cumbersome,and there is feature redundancy in training.In this study,we design arrhythmia detection based on S-shaped reconstruction and attention mechanism network SE-ResNet.This method uses a new data conversion and reconstruction algorithm-S-shaped reconstruction.The algorithm first converts a one-dimensional ECG heartbeat into a two-dimensional image using the Gram angle field.Then,the channel attention mechanism network SE-ResNet is introduced to assign different weight values to different features learned according to their importance during the learning process,thereby improving the learning effect.Finally,ten-fold cross-validation is used to evaluate the model performance and select the best hyperparameter combination to avoid model overfitting.Compared with other traditional machine learning algorithms and deep learning algorithms,this model has higher accuracy and speed,enabling rapid two-dimensional reconstruction of ECG heartbeats and the application of attention mechanisms to arrhythmia detection.(2)Aiming to solve the learning problem of massive unlabeled data,this study designs an arrhythmia detection algorithm based on self-supervised learning algorithm RMC to process ECG heartbeats of massive unlabeled data.The algorithm first converts a one-dimensional ECG heartbeat into a two-dimensional image using the Graham angle field,then uses the ViT neural network to form the MAE algorithm model.Then,introduce the Repeat module designed in this study to improve the algorithm and realize the self-supervised learning method to adjust the network parameters.Finally,a Gaussian clustering algorithm is also introduced and integrated into the loss function to further enhance the learning ability of the model.Compared with traditional supervised deep learning algorithms,the RMC algorithm is slightly less accurate,but close to their detection performance.In practical applications,it is usually necessary to deal with massive unlabeled data instead of having a large amount of labeled data to train artificial intelligence models.In this case,supervised algorithm is usually not competent,and the RMC self-supervised learning algorithm designed in this study can produce satisfactory results in this environment.